def preprocess_dir(in_dir, out_dir, GT=False):
utils.makeDirectory(out_dir)
cases = os.listdir(in_dir)
# sizes = np.zeros([len(cases) - 1, 3])
i = 0
for case in cases:
if not case.startswith('.'):
img = sitk.ReadImage(os.path.join(in_dir, case))
# label = sitk.ReadImage(os.path.join(train_dir, case))
if not GT:
img = normalizeIntensities(img)[0]
img = utils.pad_volume(img,
target_size_x=48,
target_size_y=64,
target_size_z=48,
padValue=0)
filename = case[:-7]
sitk.WriteImage(img, os.path.join(out_dir, filename + '.nrrd'))
np.save(os.path.join(out_dir, filename + '.npy'),
sitk.GetArrayFromImage(img))
def get_final_roi(img, start_x, start_y, start_z, size_x, size_y, size_z):
img_size = img.GetSize()
check_x = True if (start_x + size_x) < img_size[0] else False
check_y = True if (start_y + size_y) < img_size[1] else False
check_z = True if (start_z + size_z) < img_size[2] else False
size = [int(size_x), int(size_y), int(size_z)]
start = [int(start_x), int(start_y), int(start_z)]
if check_x and check_y and check_z:
out_img = sitk.RegionOfInterest(img, size, start)
else:
out_img = utils.pad_volume(img,
target_size_x=start_x + size_x,
target_size_y=start_y + size_y,
target_size_z=start_z + size_z,
padValue=utils.getMinimum(img))
out_img = sitk.RegionOfInterest(out_img, size, start)
return out_img
def preprocess_unlabeled(files_dir, preprocessed_dir):
utils.makedir(preprocessed_dir)
# bb_size = [80, 200, 200] # will be extended by [10,20,20] per side for augmented
# get_left_and_right_BB()
cases = sorted(os.listdir(files_dir))
cases = [x for x in cases if 'case' in x]
for i in range(0, len(cases)):
# if cases[i] == 'case_00223':
out_dir = os.path.join(preprocessed_dir, cases[i])
utils.makedir(out_dir)
print(cases[i])
img = sitk.ReadImage(
os.path.join(files_dir, cases[i], 'imaging.nii.gz'))
img = utils.resampleImage(img, SPACING, sitk.sitkLinear, 0,
sitk.sitkInt16)
# apply region of interest that is guided by bones
bb, smaller_bb = get_threshold_bounding_box(img, 500)
if img.GetSize()[0] > BB_SIZE[0]:
# if img.GetSize()[0] > BB_SIZE[1]+50: # if x-axis is very long, it might be a whole body scan
cropped_img = sitk.RegionOfInterest(
img, [smaller_bb[3], smaller_bb[4], smaller_bb[5]],
[smaller_bb[0], smaller_bb[1], smaller_bb[2]])
sitk.WriteImage(cropped_img, 'cropped_' + str(i) + '.nrrd')
lung_img = sitk.BinaryThreshold(cropped_img, -900, -700, 1,
0) # lung -900, -700
sitk.WriteImage(lung_img, 'lung_img_thresh' + str(i) + '.nrrd')
lung_img = utils.getLargestConnectedComponents(lung_img)
sitk.WriteImage(lung_img, 'lung_img_cc' + str(i) + '.nrrd')
# get boundingbox of lung:
bb_lung = getBB(lung_img, 1)
start_y_w_bb_lung = cropped_img.TransformIndexToPhysicalPoint(
[bb_lung[0], bb_lung[1], bb_lung[2]])
start_y_w_img = img.TransformPhysicalPointToIndex(
start_y_w_bb_lung)
# print('bb_lung: ', bb_lung)
# values for final ROI
start_X = max(
0, bb_lung[0] + bb_lung[3]
) # - 0.1*BB_SIZE[0]) # start 20 voxel above bottom of lung
size_X = BB_SIZE[0]
if start_X + BB_SIZE[0] > img.GetSize()[0]:
start_X = img.GetSize()[0] - size_X - 1
start_y = max(0, (start_y_w_img[1] + bb_lung[4] -
BB_SIZE[1])) ## according to bones bounding box
# start_y = max(0, bb[1] + bb[4] - BB_SIZE[1])
start_z_r = int((bb[2] + bb[5]) / 2)
start_z_l = max(0, int((bb[2] + bb[5]) / 2) - BB_SIZE[2])
else:
### pad volume in x direction
img = utils.pad_volume(img,
target_size_x=BB_SIZE[0],
padValue=utils.getMinimum(img))
# values for final ROI
start_X = 0 # start 20 voxel above bottom of lung
start_y = max(0, (bb[1] + bb[4] + 20 -
BB_SIZE[1])) ## according to bones bounding box
start_z_r = int((bb[2] + bb[5]) / 2)
start_z_l = max(0, int((bb[2] + bb[5]) / 2) - BB_SIZE[2])
left_img = get_final_roi(img, start_X, start_y, start_z_l, BB_SIZE[0],
BB_SIZE[1], BB_SIZE[2])
right_img = get_final_roi(img, start_X, start_y, start_z_r, BB_SIZE[0],
BB_SIZE[1], BB_SIZE[2])
sitk.WriteImage(left_img, os.path.join(out_dir, 'img_left.nrrd'))
sitk.WriteImage(right_img, os.path.join(out_dir, 'img_right.nrrd'))
def preprocess():
utils.makedir(preprocessed_dir)
bb_size = [80, 200,
200] # will be extended by [10,20,20] per side for augmented
# get_left_and_right_BB()
cases = sorted(os.listdir(files_dir))
cases = [x for x in cases if 'case' in x]
bbs_left = np.load('bbs_left.npy')
bbs_right = np.load('bbs_right.npy')
for i in range(0, len(cases)):
# if cases[i] == 'case_00002':
out_dir = os.path.join(preprocessed_dir, cases[i])
utils.makedir(out_dir)
print(cases[i])
img = sitk.ReadImage(
os.path.join(files_dir, cases[i], 'imaging.nii.gz'))
img = utils.resampleImage(img, [3.0, 0.75, 0.75], sitk.sitkLinear, 0,
sitk.sitkInt16)
segm = sitk.ReadImage(
os.path.join(files_dir, cases[i], 'segmentation.nii.gz'))
# apply region of interest that is guided by bones
bb, smaller_bb = get_threshold_bounding_box(img, 500)
if bb[3] > 100:
cropped_img = sitk.RegionOfInterest(
img, [smaller_bb[3], smaller_bb[4], smaller_bb[5]],
[smaller_bb[0], smaller_bb[1], smaller_bb[2]])
# sitk.WriteImage(cropped_img, 'cropped_' + str(i) + '.nrrd')
lung_img = sitk.BinaryThreshold(cropped_img, -900, -700, 1,
0) # lung -900, -700
# sitk.WriteImage(lung_img, 'lung_img_thresh' + str(i) + '.nrrd')
lung_img = utils.getLargestConnectedComponents(lung_img)
# sitk.WriteImage(lung_img, 'lung_img_cc' + str(i) + '.nrrd')
# get boundingbox of lung:
bb_lung = getBB(lung_img, 1)
# print('bb_lung: ', bb_lung)
# values for final ROI
start_X = max(0, bb_lung[0] + bb_lung[3] -
20) # start 20 voxel above bottom of lung
size_X = 100
if start_X + 100 > img.GetSize()[0]:
start_X = img.GetSize()[0] - size_X - 1
img = sitk.RegionOfInterest(
img, [size_X, img.GetSize()[1],
img.GetSize()[2]], [start_X, 0, 0])
# img = sitk.RegionOfInterest(img, [size_X, bb[4], bb[5]],
# [start_X, bb[1], bb[2]])
sitk.WriteImage(img, 'final_cropped_' + str(i) + '.nrrd')
else:
### pad volume in x direction
img = utils.pad_volume(img,
target_size_x=100,
padValue=utils.getMinimum(img))
## split image in left and right half
[size_x, size_y, size_z] = img.GetSize()
img_left = sitk.RegionOfInterest(
img, [size_x, size_y, int(size_z / 2)], [0, 0, 0])
img_right = sitk.RegionOfInterest(
img, [size_x, size_y, int(size_z / 2)],
[0, 0, int(size_z / 2) - 1])
sitk.WriteImage(img_left, 'img_left_temp.nrrd')
sitk.WriteImage(img_right, 'img_right_temp.nrrd')
# get ROI for left and right kidney
roi_left_start = [
max(0, bbs_left[i, 0] - 10),
max(0, bbs_left[i, 1] - 10),
max(0, bbs_left[i, 2] - 10)
]
roi_right_start = [
max(0, bbs_right[i, 0] - 10),
max(0, bbs_right[i, 1] - 10),
max(0, bbs_right[i, 2] - 10)
]
# apply ROI to image
img_left_final = get_final_roi(img_left, roi_left_start[0],
roi_left_start[1], roi_left_start[2],
bb_size[0] + 20, bb_size[1] + 20,
bb_size[2] + 20)
img_right_final = get_final_roi(img_right, roi_right_start[0],
roi_right_start[1], roi_right_start[2],
bb_size[0] + 20, bb_size[1] + 20,
bb_size[2] + 20)
segm_left_final = utils.resampleToReference(segm,
img_left_final,
sitk.sitkNearestNeighbor,
defaultValue=0,
out_dType=sitk.sitkUInt8)
segm_right_final = utils.resampleToReference(segm,
img_right_final,
sitk.sitkNearestNeighbor,
defaultValue=0,
out_dType=sitk.sitkUInt8)
# write images
sitk.WriteImage(img_left_final, os.path.join(out_dir, 'img_left.nrrd'))
sitk.WriteImage(img_right_final, os.path.join(out_dir,
'img_right.nrrd'))
sitk.WriteImage(segm_left_final, os.path.join(out_dir,
'segm_left.nrrd'))
sitk.WriteImage(segm_right_final,
os.path.join(out_dir, 'segm_right.nrrd'))
def get_left_and_right_BB():
cases = sorted(os.listdir(files_dir))
cases = [x for x in cases if 'case' in x]
bbs_left = np.zeros([len(cases), 6])
bbs_right = np.zeros([len(cases), 6])
worried_cases = [
'case_00048', 'case_00097', 'case_00178', 'case_00185', 'case_00191'
]
for i in range(0, len(cases)):
# if cases[i] in worried_cases:
# if cases[i] == 'case_00001' or cases[i] == 'case_00011':
print(cases[i])
img = sitk.ReadImage(
os.path.join(files_dir, cases[i], 'imaging.nii.gz'))
img = utils.resampleImage(img, [4.0, 1.0, 1.0], sitk.sitkLinear, 0,
sitk.sitkInt16)
segm = sitk.ReadImage(
os.path.join(files_dir, cases[i], 'segmentation.nii.gz'))
# apply region of interest that is guided by bones
bb, smaller_bb = get_threshold_bounding_box(img, 500)
if bb[3] > 100:
cropped_img = sitk.RegionOfInterest(
img, [smaller_bb[3], smaller_bb[4], smaller_bb[5]],
[smaller_bb[0], smaller_bb[1], smaller_bb[2]])
# sitk.WriteImage(cropped_img, 'cropped_' + str(i) + '.nrrd')
lung_img = sitk.BinaryThreshold(cropped_img, -900, -700, 1,
0) # lung -900, -700
# sitk.WriteImage(lung_img, 'lung_img_thresh' + str(i) + '.nrrd')
lung_img = utils.getLargestConnectedComponents(lung_img)
# sitk.WriteImage(lung_img, 'lung_img_cc' + str(i) + '.nrrd')
# get boundingbox of lung:
bb_lung = getBB(lung_img, 1)
# print('bb_lung: ', bb_lung)
# values for final ROI
start_X = max(0, bb_lung[0] + bb_lung[3] -
20) # start 20 voxel above bottom of lung
size_X = 100
if start_X + 100 > img.GetSize()[0]:
start_X = img.GetSize()[0] - size_X - 1
# img_final = sitk.RegionOfInterest(img, [size_X, bb[4], bb[5]],
# [start_X, bb[1], bb[2]])
img_final = sitk.RegionOfInterest(
img, [size_X, img.GetSize()[1],
img.GetSize()[2]], [start_X, 0, 0])
# sitk.WriteImage(cropped_img_final, 'final_cropped_' + str(i) + '.nrrd')
else:
### pad volume in x direction
# get minimum value for padding
filter = sitk.StatisticsImageFilter()
filter.Execute(img)
minValue = filter.GetMinimum()
img_final = utils.pad_volume(img,
target_size_x=100,
padValue=minValue)
## split image in left and right half
[size_x, size_y, size_z] = img_final.GetSize()
img_left = sitk.RegionOfInterest(
img_final, [100, size_y, int(size_z / 2)], [0, 0, 0])
img_right = sitk.RegionOfInterest(
img_final, [100, size_y, int(size_z / 2)],
[0, 0, int(size_z / 2) - 1])
bb_left, bb_right = getBoundingBoxes(img_left, img_right, segm)
bbs_left[i] = [
bb_left[0], bb_left[1], bb_left[2], bb_left[3], bb_left[4],
bb_left[5]
]
bbs_right[i] = [
bb_right[0], bb_right[1], bb_right[2], bb_right[3], bb_right[4],
bb_right[5]
]
print(bb_left, bb_right)
np.save('bbs_left.npy', bbs_left)
np.save('bbs_right.npy', bbs_right)
print('max and min x: ', np.amin(bbs_left[:, 0]), np.amax(bbs_left[:, 3]))
print('max and min y: ', np.amin(bbs_left[:, 1]), np.amax(bbs_left[:, 4]))
print('max and min z: ', np.amin(bbs_left[:, 2]), np.amax(bbs_left[:, 5]))
print('max and min x: ', np.amin(bbs_right[:, 0]), np.amax(bbs_right[:,
3]))
print('max and min y: ', np.amin(bbs_right[:, 1]), np.amax(bbs_right[:,
4]))
print('max and min z: ', np.amin(bbs_right[:, 2]), np.amax(bbs_right[:,
5]))